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Rapha – your startup's first recruiter

Hacker News

Rapha – your startup's first recruiter

What'up HN! My name is Masoud, Founder of Rapha (www.withrapha.com) Getting close to our pilot program launch for Rapha, thought I give y'all a sneak peak. Check out Rapha in action If this is your first time hearing about us, let me get you up to speed. We're a end-to-end ATS (Applicant Tracking System) aiming to being a startup's first recruiter + recruiter's first co-pilot. -- For decades, recruiting has been haunted by the boogeyman...and that boogeyman is called qualifying. As the referral network dries up and forces recruiters, hiring managers, and founders, to start prospecting...all they have to go off of are resumes + LinkedIns + their judgement = hoping for the best lol. Applicants on the other hand are struggling with trying to separate themselves from the rest of the pack. On paper...everyone looks the same...but the resume has failed to capture that one attribute that has significant impact on their hiring decision....and that is the applicant's origin story. For the very first time, companies are now able to ask those burning questions up front. Recruiters and HMs can now minimize time-spent in calibration meetings and focus on being present for the applicant. You can ask engineers "how would go about debugging this line of code" ask a product designer, "Walk me through your design process"...and listen to their walk through and rationale. Resulting in redefining what the "first call" means and for others, skipping the first call and jumping straight into the details! The choice is yours on how you want to leverage Rapha. Which ever way you do, one thing is for certain... -- Now for some FAQs: 1. "Will passive applicants apply to roles when seeing the audio questions?" - Yes. We learned that applicants of all kinds (inbound, referral, outbound) enjoy the thought of accelerating the process by answering questions coming directly from the HM/Founder. 2. "Will Rapha be replacing recruiters + sourcers?" - No. Rapha can be leveraged in so many ways by recruiters. Whether you're a solo-recruiter at a startup or have a full recruiting team. We empower them to be present with their applicants instead of playing keyword bingo all day. 3. "Will engineers + technical people apply if they see this?" - They will. Because we are doing the inevitable -- you're gonna need to speak to someone eventually and it's in the applicants best interest to control the narrative :). -- Our Demo: You can check the demo here: https://x.com/MasoudByDesign/status/1733690815313268936?s=20 Check out our job application we have open to join: https://app.withrapha.com/job/134?r=698c84e9-0415-43d5-acba-... Thanks again for everyone's time...I'm here if you need me :)

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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